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Topic 1 DQ 2

Aug 11-15, 2022

Assume you wish to study the influence of high school principals’ leadership styles and academic achievement in their schools in your state. Do you need primary data, secondary data, or both? Explain. What logistic difficulties do you expect in gathering all necessary data? Explain. How are you going to combine all data into a single file for statistical analysis?

Maren

I would prefer to complete the study using primary data. With primary research data, the researcher designs a research project to address the question and collects and analyzes the data to address specific research questions (Wickham, 2019). I personally would tend to gravitate to primary data because I would already know what I am searching for as I gather and assess data. I wouldn’t have to filter through someone else’s interpretations of what a high school principal’s leadership style should look like and what should be accepted and appropriate for academic achievement.

Although primary would be my preference, I know that it might not always be possible. Having a mixture of primary and secondary data would be a good alternative. By using secondary data, a researcher can gather reliable data that could prove to be meaningful to the research. Secondary data can be beneficial to a study, but there are also possible limitations. One such limitation is that secondary data comes with its own biases, which researchers should be careful in interpreting (Trinh, 2018). Overall, secondary data could be useful in gaining necessary knowledge. Researchers just need to be aware of how their own perceptions affect how the data is represented.

Trinh, Q. D. (2018, April). Understanding the impact and challenges of secondary data analysis. In Urologic Oncology: Seminars and original investigations (Vol. 36, No. 4, pp. 163-164). Elsevier.

Wickham, R. J. (2019). Secondary analysis research. Journal of the advanced practitioner in oncology, 10(4), 395.

Sarah

Primary data is the most frequently used in research (Casteel, 2021). It gathers data from sources for the purpose of conducting research, using the unit of observation (source of the data), and recording the data in a way that it could be analyzed and used for interpretation. Secondary data is collected by another source outside of the researcher, for a purpose other than the original intended use (Casteel, 2021). In this case, both types of data could be useful. The goal of the primary quantitative data is to take the results of a sample, in this case, an assessment of the influence of a high school principal's leadership style and its effect on academic achievement and, and how it could be applied to a larger population(Greenberger & Miron, 2021) of high school principals, statewide or otherwise. I want to be able to generalize those results from the sample in my study to the larger principal population, to determine the effectiveness of the study. It could be a challenge to do so, as every high school is dealing with different challenges and circumstances.

There are so many different variables that affect outcomes in a high school- depending on the financial, academic, parent, etc. support a community and school have, the principal may have very different experiences. The secondary data set may try to get around some of the dependent and independent variables, considering the dependability, reliability, and validity of the instruments used in varying schools (Casteel, 2021). The utilization of a quantitative study must assess the generalizability (Greenberger and Miron, 2021). In combining the data into a single useful fine, the most important goal is that the unit of analysis (or principal in this case) corresponds with the data (Casteel, 2021), merging the primary and secondary sources match and merge for statistical significance. (Casteel, 2021).

Casteel, A. (2021). Populations and Samples in Quantitative Research. Grand Canyon University (Ed.), GCU Doctoral Research: Introduction to Sampling, Data Collection, and Data Analysis (1st ed).

Greenberger, S. & Miron, D. (2021). Introduction. In Grand Canyon University (Ed.), GCU Doctoral Research: Introduction to Sampling, Data Collection, and Data Analysis (1st ed).

Topic 1 DQ 1

Aug 11-13, 2022

Quantitative research tends to require the use of relatively large samples. With that in mind, consider the strengths and weaknesses of purposeful, convenience, and random sampling approaches in quantitative research. Assume that you are an automobile manufacturing executive tasked with increasing sales in your state. You wish to evaluate the effectiveness of an incentive program for sales personnel implemented at 10 dealerships in medium-size cities and 10 dealerships in small cities. All you have at hand are archived records of the incentives received by the sales staff and of their respective sales transactions. What information, data, and variables do you choose to analyze as relevant to your evaluation? Why? Which of the GCU core quantitative designs (introduced in a previous course) would best fit your evaluation plan? Why? How much data do you need to analyze in order to reach a meaningful conclusion? Explain. Do you anticipate any logistic difficulties or ethical concerns? Explain.

REPLY TO DISCUSSION

Kaleah

, data and variables pertaining to the effectiveness of an incentive program need to be evaluated in order to determine what direction to go in your research. The data and variables chosen to analyze should enhance the study of the automobile manufacturing company by providing the accuracy and rigor needed to construct conclusions upon which the researcher and others can rely (Asmus & Radocy, 2017). Additionally, the method of collecting data should accurately cover the target population. The unit of analysis covers large populations and is designed to divulge underlying patterns, trends and relationships of the study’s contextual situations (Albers, 2017). The quantitative design that fits the evaluation plan is the descriptive design due to the collection of data of the target population and the method to accurately describe the target population and the effectiveness of an incentive program (Bloomfield & Fisher, 2019). Difficulties or ethical concerns to be mindful of are cultural implications and how recent the data collected is.

Albers, M. J. (2017). Quantitative data analysis—In the graduate curriculum. Journal of Technical Writing and Communication, 47(2), 215-233.

Asmus, E. P., & Radocy, R. E. (2017). Quantitative analysis. In Critical Essays in Music Education (pp. 129-172). Routledge.

Bloomfield, J., & Fisher, M. J. (2019). Quantitative research design. Journal of the Australasian Rehabilitation Nurses Association, 22(2), 27-30.

Maren

Because the data that has been provided comes from events that have already occurred, it is important to take advantage of the information that is available. This is one example of why it is so important to choose the most appropriate research design. Research designs are the plans or blueprints that will help researchers answer the specific questions they have about their study (Bloomfield & Fisher, 2019). In order to take full advantage of the available data to answer the research question, I believe that the descriptive quantitative research design would be the most effective. The purpose of the descriptive design is to describe conditions, events, or individuals by studying them as they are (Siedlecki, 2020). This would be the most beneficial of all the designs to use because it is non-experimental, has the ability to study events that have already occurred, and can work with research problems that have one or more variables. It is also able to lay the foundation for any quasi-experimental or experimental studies (2020).

Bloomfield, J., & Fisher, M. J. (2019). Quantitative research design. Journal of the Australasian Rehabilitation Nurses’ Association (JARNA), 22(2), 27–30. https://doi-org.lopes.idm.oclc.org/10.33235/jarna.22.2.27-30

Siedlecki, S. (2020). Understanding descriptive research designs and methods. Clinical Nurse Specialist, 34 (1), 8-12. doi: 10.1097/NUR.0000000000000493.

Tonyi

What information, data, and variables do you choose to analyze as relevant to your evaluation? I would like to analyze the consistency of the program, and possibly some of the questions I would look at or attempt to identify would be, was the incentive the same each time? Did one individual receive more than the other, where the managers held to some type of consistency? Did one manager give more incentives out than others? Is the product in demand in the small cities, how big is the staff in both areas, was the quantity of the product available at all times for both facilities, and how was the incentive marketed for the staff?

Which of the GCU core quantitative designs (introduced in a previous course) would best fit your evaluation plan? I believe that the Grounded Theory Design(Greenberger& Miron, 2021), primarily to capture an understanding of the issue through the data, would be my only choice because the only information that was provided to me is very limited.

How much data do you need to analyze in order to reach a meaningful conclusion? I would need to see how many participants were included in the incentive program. Were the requirements the same or different due to the population size of the communities? If the incentives were given on a percentage of sales basis?

Do you anticipate any logistic difficulties or ethical concerns? There are always logistic and ethical concerns in any study. Was the data valid, and should it be a part of the inclusion criteria (Casteel, 2021), taken and recorded by a reliable individual in the system? Did the inventory of the sales personnel stay stocked in one area more than another? Was the product in demand in one area more than another? I once worked for a company where some of the sales individuals gave reception or greeters a kickback for any sale they made if the receptionist referred them to the salesperson, so the receptionist took all prospects to those individuals first.

Greenberger, S. and Miron, D. (2021). Introduction. In Grand Canyon University (Ed.), GCU Doctoral Research: Introduction to Sampling, Data Collection, and Data Analysis (1st ed).

Casteel, A. (2021). Populations and Samples in Quantitative Research. Grand Canyon University (Ed.), GCU Doctoral Research: Introduction to Sampling, Data Collection, and Data Analysis (1st ed).

What information, data, and variables do you choose to analyze as relevant to your evaluation? Why? Which of the GCU core quantitative designs (introduced in a previous course) would best fit your evaluation plan? Why? How much data do you need to analyze in order to reach a meaningful conclusion? Explain. Do you anticipate any logistic difficulties or ethical concerns? Explain.

The information, data, and variables I chose to analyze in this case must be a reflection of the goals I want to achieve. I want to evaluate the effectiveness of previous incentive programs, then I will need to know what they are, how they worked, and what their outcomes were before making a decision about what will work for my own study and research goals. The goal of a quantitative study is to take the results of a sample group and extrapolate them to the rest of a population, and in this case to the previous attempts at incentivizing the employees to increase sales (Greenberger & MIron, 2021). A quantitative study makes the information generalizable to the rest of the population, even a specific part of the incentive program that worked, and transfers that data to the next planned evaluation (Greenberger and Miron, 2021). The instrument chosen for quantitative data, in this case, could be a questionnaire and survey approach in a possible causal-comparative approach, understanding the what and why of incentives and how they drive sales in the dealership. This would give meaning to the relationship between the independent and dependent variables and could give direction to the next questionnaire survey attempt. In order to be statistically significant, the size of the group we are observing must meet the purpose of the study determining if there is a significant difference between the previous attempts at incentives, with a result that is limited to a 0.05 margin of error (Casteel, 2021) to determine a false-positive.

Greenberger, S. and Miron, D. (2021). Introduction. In Grand Canyon University (Ed.), GCU Doctoral Research: Introduction to Sampling, Data Collection, and Data Analysis (1st ed).

Casteel, A. (2021). Populations and Samples in Quantitative Research. Grand Canyon University (Ed.), GCU Doctoral Research: Introduction to Sampling, Data Collection, and Data Analysis (1st ed).

Larry

The process of collection of data and sampling is very important and input-to-business stability and stabilization are examined for sampled-data systems under deterministic aperiodic sampling and random sampling, respectively. Using the direct design method, the sampled-data systems are transformed into switched systems with switched time-varying delays. (Zhao et al,.2021) these companies collected data and the collection process might have been flawed by bias by workers of the dealerships.

The reason for this is making a comparative study on both dealerships and making a comparative study on sales, this might also be because of information collection and analysis. There are now ways to avoid biased with recent technology, like the use of AI, which uses data to predict processes. However, due to AI's prediction of future behavior, it is highly susceptible to data tampering from adversaries who may flood the program with false information. Previous solutions have utilized random sampling, active learning, blockchain, and human interaction to solve AI bias, thus, the proper use of AI, might greatly reduce bias in the process of collection and analyzing the data. (Obaidat et al,.2021).

A lot of information is needed to be analyzed to reach a conclusion, data from the past, present, and archives are all needed to make up a proper amount of information to come out with a proper, availability of data related to the employment relationship has ballooned into an unruly mass of performance metrics, personal characteristics, biometric recordings, and creative output, information gathered or taken from these workers could also be gotten by volunteering participation or by information gotten from the dealership without the knowledge of the employees. (Bodie,.2022).

The handling of the information gotten from employees might also be very sensitive and needed some form of protection and thus needed to be coded to avoid issues with employees and employers.

References

BODIE, M. T. (2022). The Law of Employee Data: Privacy, Property, Governance. Indiana Law Journal, 97(2), 707–754.

Obaidat, M., Singh, N., & Vergara, G. (2021). Artificial Intelligence Bias Minimization Via Random Sampling Technique of Adversary Data. 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC), Computing and Communication Workshop and Conference (CCWC), 2021 IEEE 11th Annual, 1226–1230. https://doi-org.lopes.idm.oclc.org/10.1109/CCWC51732.2021.9375929

Zhao, P., Niu, B., Feng, W., & Yan, Z. (2021). Input-to-State Stability and Stabilization of Sampled-Data Systems Under Aperiodic Sampling and Random Sampling. IEEE Access, Access, IEEE, 9, 47657–47667. https://doi-org.lopes.idm.oclc.org/10.1109/ACCESS.2021.3058153

Bertha

What information, data, and variables do you choose to analyze as relevant to your evaluation? Why?

The scenario has incentives as a variable, the records provide data, and there are two dealerships that are involved in the study. The sampling has many participants (Ajibade, & Salako, 2021). The weaknesses could be that some of the participants decide to take part in the study. Participants’ feedback is not provided with specific details that impart knowledge that could be beneficial (Ajibade, & Salako, 2021). The quantitative study is objective. The strength of the study might be reflected through the large number of participants to represent a larger group of workers within the same occupation to provide general data.

Which of the GCU core quantitative designs (introduced in a previous course) would best fit your evaluation plan?

The objective is to make comparisons of the dealerships and if incentives cause increased sales for the company. The causal-comparative design is chosen because there are two various groups being compared with one variable being considered. The questions are to be aligned with the problem and purpose statements. The hypothesis statements have a null statement and an alternative statement that address the problem (Ajibade, & Salako, 2021).

How much data do you need to analyze in order to reach a meaningful conclusion? Explain. Do you anticipate any logistic difficulties or ethical concerns? Explain.

The data from the archives and the present data are needed in order to make comparison. There might be some difficulty collecting all the necessary data for analysis. If the participants decide not to follow through with the study because of questions that are being asked (Dana, Tajpour, Salamzadeh, Hosseini, & Zolfaghari, 2021). Anonymity could pose a problem because of so many individuals involved. The coding process needs to be developed to protect the participants’ privacy.

Ajibade, O. E., & Salako, O. A. (2021). Incentive schemes and employees’ productivity in private organisations in Nigeria. Journal of Public Administration, Finance & Law, 22, 140–155. https://doi-org.lopes.idm.oclc.org/10.47743/jopafl-2021-22-10

Dana, L.-P., Tajpour, M., Salamzadeh, A., Hosseini, E., & Zolfaghari, M. (2021). The impact of entrepreneurial education on technology-based enterprises development: The mediating role of motivation. Administrative Sciences (2076-3387), 11(4), 105. https://doi-org.lopes.idm.oclc.org/10.3390/admsci11040105